Consensus-Based Best Practice Recommendations for Myelofibrosis Management in Routine Clinical Practice with a Focus on Patients with Cytopenias: Systematic Literature Review and Clinical Practice Recommendations from a Global Consensus Group
Bibliographic record
Abstract
Background: Myelofibrosis (MF) is a BCR-ABL1-negative myeloproliferative neoplasm (MPN) characterized by progressive bone marrow fibrosis, splenomegaly, and cytopenias due to impaired hematopoiesis (Passamonti and Mora. Blood 2022). Despite the availability of comprehensive national and international guidelines for diagnosing and managing MF, gaps remain in translating these guidelines into clinical practice, particularly for patients with cytopenias, non-response or intolerance to Janus kinase (JAK) inhibitor treatment, and those ineligible for clinical trial enrollment. Consequently, an international expert consensus group was established with the objective of augmenting the existing guidance. Objectives: To develop a set of evidence- and consensus-based recommendations for managing MF in routine clinical practice, with a focus on patients with cytopenias, including practical strategies and tools to support clinicians. Methods : Modified Delphi methodology was utilized to achieve consensus (Figure 1). A Steering Committee (SC) of 9 expert hematologists (the authors) was established. During an initial meeting, the SC proposed 25 clinical questions that addressed key issues across 5 consensus themes: 1) Defining the thresholds for anemia and when to initiate/modify treatment; 2) defining the threshold for thrombocytopenia and when to initiate/modify treatment; 3) defining JAK inhibitor failure and what would warrant switching treatment; 4) how and when to determine prognosis in patients with MF; and, 5) unmet needs in MF clinical trials. An extended faculty (EF) was then enlisted, comprising hematologists and patient advocacy groups, who voted on the importance of the questions to address. The 15 highest scoring questions were selected for the consensus program (Table 1). To gather scientific evidence around the questions, a systematic literature review (SLR) was conducted using the PubMed and Embase databases, adhering to a PICO (Population, Intervention, Comparison, Outcome) framework. In a subsequent meeting, recommendations were formulated to address the questions using evidence from the SLR and the expert clinical experience of the SC. An online voting platform was then used for both the SC and EF to provide an agreement score for each recommendation. Consensus was achieved when 75% of the respondents agreed within the range of 7-9 on a 9-point scale (1=strongly disagree, 9=strongly agree). Results: Consensus was achieved among voters (hematologists [n=29] and patients [n=9] from Europe, the United States, Canada, Australia, and Israel) for all 15 recommendations. Recommendations in theme 1 emphasize the importance of comprehensive evaluation, exclusion of other causes of anemia, therapy dose optimization, and consideration of additional treatments for managing anemia in patients with MF. Recommendations in theme 2 highlight the complexity of managing splenomegaly, symptoms, and anemia in patients with low platelet counts, and factors guiding therapy selection for these patients. Theme 3 recommendations discuss criteria used to determine JAK inhibitor failure, including relapse, refractoriness, or intolerance, and guidance for distinguishing between these. In theme 4, recommendations address the use of validated prognostic scores at diagnosis and during the disease course, and transplantation risk assessment. The limitations and appropriate utilization of these scores are emphasized, and the need to develop prognostic scores in pre-primary MF is highlighted. Theme 5 recommendations cover the importance of addressing unmet needs in MF clinical trials, and include striving for inclusivity by removing barriers to the participation of underserved patient populations and focusing efforts on validating additional endpoints beyond traditional measures. Conclusions: An international panel of physicians with expertise in MF, together with a diverse EF, was able to achieve a high level of consensus across a wide range of critical gaps in MF management. These recommendations provide a valuable framework to support clinicians in optimizing care for patients with MF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.196 | 0.321 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.028 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".